Reconstructing contact network structure and cross-immunity patterns from multiple infection histories.

Interactions within a population shape the spread of infectious diseases but contact patterns between individuals are difficult to access. We hypothesised that key properties of these patterns can be inferred from multiple infection data in longitudinal follow-ups. We developed a simulator for epide...

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Autores principales:	Christian Selinger, Samuel Alizon
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/b5ae857954894724b98188715b7c3731
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spelling	oai:doaj.org-article:b5ae857954894724b98188715b7c37312021-12-02T19:57:47ZReconstructing contact network structure and cross-immunity patterns from multiple infection histories.1553-734X1553-735810.1371/journal.pcbi.1009375https://doaj.org/article/b5ae857954894724b98188715b7c37312021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009375https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Interactions within a population shape the spread of infectious diseases but contact patterns between individuals are difficult to access. We hypothesised that key properties of these patterns can be inferred from multiple infection data in longitudinal follow-ups. We developed a simulator for epidemics with multiple infections on networks and analysed the resulting individual infection time series by introducing similarity metrics between hosts based on their multiple infection histories. We find that, depending on infection multiplicity and network sampling, multiple infection summary statistics can recover network properties such as degree distribution. Furthermore, we show that by mining simulation outputs for multiple infection patterns, one can detect immunological interference between pathogens (i.e. the fact that past infections in a host condition future probability of infection). The combination of individual-based simulations and analysis of multiple infection histories opens promising perspectives to infer and validate transmission networks and immunological interference for infectious diseases from longitudinal cohort data.Christian SelingerSamuel AlizonPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1009375 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Christian Selinger Samuel Alizon Reconstructing contact network structure and cross-immunity patterns from multiple infection histories.
description	Interactions within a population shape the spread of infectious diseases but contact patterns between individuals are difficult to access. We hypothesised that key properties of these patterns can be inferred from multiple infection data in longitudinal follow-ups. We developed a simulator for epidemics with multiple infections on networks and analysed the resulting individual infection time series by introducing similarity metrics between hosts based on their multiple infection histories. We find that, depending on infection multiplicity and network sampling, multiple infection summary statistics can recover network properties such as degree distribution. Furthermore, we show that by mining simulation outputs for multiple infection patterns, one can detect immunological interference between pathogens (i.e. the fact that past infections in a host condition future probability of infection). The combination of individual-based simulations and analysis of multiple infection histories opens promising perspectives to infer and validate transmission networks and immunological interference for infectious diseases from longitudinal cohort data.
format	article
author	Christian Selinger Samuel Alizon
author_facet	Christian Selinger Samuel Alizon
author_sort	Christian Selinger
title	Reconstructing contact network structure and cross-immunity patterns from multiple infection histories.
title_short	Reconstructing contact network structure and cross-immunity patterns from multiple infection histories.
title_full	Reconstructing contact network structure and cross-immunity patterns from multiple infection histories.
title_fullStr	Reconstructing contact network structure and cross-immunity patterns from multiple infection histories.
title_full_unstemmed	Reconstructing contact network structure and cross-immunity patterns from multiple infection histories.
title_sort	reconstructing contact network structure and cross-immunity patterns from multiple infection histories.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/b5ae857954894724b98188715b7c3731
work_keys_str_mv	AT christianselinger reconstructingcontactnetworkstructureandcrossimmunitypatternsfrommultipleinfectionhistories AT samuelalizon reconstructingcontactnetworkstructureandcrossimmunitypatternsfrommultipleinfectionhistories
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Reconstructing contact network structure and cross-immunity patterns from multiple infection histories.

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